#不稳定输出，有时有错误，废弃
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

DIR = "/Users/michael/codes_ai/python/ai_demo_nlp_python/files/"
model_dir="/Users/michael/.cache/modelscope/hub/models/Qwen/Qwen2___5-VL-3B-Instruct"
pic_fire_stair= "%sfire_stairs.webp" % DIR
pic_fire_woods= "%sfire_woods.webp" % DIR

# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_dir, torch_dtype="auto", device_map="auto"
)

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen2.5-VL-7B-Instruct",
#     torch_dtype=torch.bfloat16, //如果推理太慢也可以启用半精度来加速
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

# default processer
min_pixels = 256 * 28 * 28  #这里做了推理时候的图片压缩,平衡精度和速度,默认是全尺寸图片进行推理,很慢也要很大的显存
max_pixels = 1280 * 28 * 28
processor = AutoProcessor.from_pretrained(
    model_dir, min_pixels=min_pixels, max_pixels=max_pixels
)

# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": pic_fire_woods, #这里填入你要推理的图片路径,也可以是url
            },
            {"type": "text", "text": "详细描述一下这张图片"}, #这里是问题
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
#inputs = inputs.to("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

# ['这张图片展示了一群孩子，他们脸上和衣服上沾满了泥土。背景似乎是一个户外的环境，可能是农村或贫困地区的场景。孩子们的表情各异，有的微笑，有的严肃，
# 但整体氛围显得有些沉重。\n\n图片上方有一段中文文字：“所有人都告诉我要懂事，却没有人告诉我要开心。” 这句话传达了对成年人社会中的一种普遍现象的反思：
# 人们往往被教导要懂事、成熟，但在快乐和自我满足方面却很少得到关注和支持。\n\n图片下方有英文翻译：“Everyone told me to be sensible. But no one told me to be happy.” 这进一步强调了']
